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Back to termination

Explore the benefits of monotonicity in flow functions and fixed point computation for termination guarantees. Learn about iterative algorithms, lattice height, and the least fixed point concept in program analysis. Discover how starting at the top with a global flow function can lead to the greatest fixed point. Dive into examples of constant propagation and precise fixed point computations.

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Back to termination

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  1. Back to termination • So if F is monotonic, we have what we want: finite height ) termination, without the outer join • Also, if the local flow functions are monotonic, then global flow function F is monotonic

  2. Another benefit of monotonicity • Suppose Marsians came to earth, and miraculously give you a fixed point of F, call it fp. • Then:

  3. Another benefit of monotonicity • Suppose Marsians came to earth, and miraculously give you a fixed point of F, call it fp. • Then:

  4. Another benefit of monotonicity • We are computing the least fixed point...

  5. Recap • Let’s do a recap of what we’ve seen so far • Started with worklist algorithm for reaching definitions

  6. Worklist algorithm for reaching defns let m: map from edge to computed value at edge let worklist: work list of nodes for each edge e in CFG do m(e) := ; for each node n do worklist.add(n) while (worklist.empty.not) do let n := worklist.remove_any; let info_in := m(n.incoming_edges); let info_out := F(n, info_in); for i := 0 .. info_out.length do let new_info := m(n.outgoing_edges[i]) [ info_out[i]; if (m(n.outgoing_edges[i])  new_info]) m(n.outgoing_edges[i]) := new_info; worklist.add(n.outgoing_edges[i].dst);

  7. Generalized algorithm using lattices let m: map from edge to computed value at edge let worklist: work list of nodes for each edge e in CFG do m(e) := ? for each node n do worklist.add(n) while (worklist.empty.not) do let n := worklist.remove_any; let info_in := m(n.incoming_edges); let info_out := F(n, info_in); for i := 0 .. info_out.length do let new_info := m(n.outgoing_edges[i]) t info_out[i]; if (m(n.outgoing_edges[i])  new_info]) m(n.outgoing_edges[i]) := new_info; worklist.add(n.outgoing_edges[i].dst);

  8. Next step: removed outer join • Wanted to remove the outer join, while still providing termination guarantee • To do this, we re-expressed our algorithm more formally • We first defined a “global” flow function F, and then expressed our algorithm as a fixed point computation

  9. Guarantees • If F is monotonic, don’t need outer join • If F is monotonic and height of lattice is finite: iterative algorithm terminates • If F is monotonic, the fixed point we find is the least fixed point.

  10. What about if we start at top? • What if we start with >: F(>), F(F(>)), F(F(F(>)))

  11. What about if we start at top? • What if we start with >: F(>), F(F(>)), F(F(F(>))) • We get the greatest fixed point • Why do we prefer the least fixed point? • More precise

  12. Graphically y 10 10 x

  13. Graphically y 10 10 x

  14. Graphically y 10 10 x

  15. Graphically, another way

  16. Another example: constant prop • Set D = in x := N Fx := N(in) = out in x := y op z Fx := y op z(in) = out

  17. Another example: constant prop • Set D = 2 { x ! N | x 2 Vars Æ N 2 Z } in x := N Fx := N(in) = in – { x ! * } [ { x ! N } out in x := y op z Fx := y op z(in) = in – { x ! * } [ { x ! N | ( y ! N1 ) 2 in Æ ( z ! N2 ) 2 in Æ N = N1 op N2 } out

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